from tensorflow.keras.datasets import boston_housing
from tensorflow.keras import models
from tensorflow.keras import layers
import numpy as np

(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()

# print(train_data.shape)
# print(test_data.shape)

mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std

test_data -= mean
test_data /= std


def build_model():
    model = models.Sequential()
    model.add(layers.Dense(64, activation='relu',
                           input_shape=(train_data.shape[1],)))
    model.add(layers.Dense(64, activation='relu'))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop', loss='mse', metrics=['mae'])
    return model

# K折验证


k = 4
num_val_samples = len(train_data) // k
num_epochs = 100
all_scores = []

for i in range(k):
    print('processing fold #', i)
    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]
    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]

    partial_train_data = np.concatenate(
        [train_data[:i * num_val_samples],
         train_data[(i + 1) * num_val_samples:]],
        axis=0
    )
    partial_train_targets = np.concatenate(
        [train_targets[: i * num_val_samples],
         train_targets[(i + 1) * num_val_samples:]],
        axis=0
    )

    model = build_model()
    model.fit(
        partial_train_data,
        partial_train_targets,
        epochs=num_epochs,
        batch_size=1,
        verbose=0
    )
    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)
    all_scores.append(val_mae)

print(np.mean(all_scores))
